scholarly journals Probabilistic Inventory Model Multi-Source Backlogged Probabilistic Inventory Model for Crisp and Fuzzy Environment

2018 ◽  
Vol 15 ◽  
pp. 8051-8069
Author(s):  
H. A. Ferganya ◽  
Osama Mahmoud Hollah
Keyword(s):  
Random Variable ◽  
Inventory Model ◽  
Inventory Level ◽  
Periodic Review ◽  
Fuzzy Environment ◽  
Holding Cost ◽  
Annual Total ◽  
Probabilistic Inventory Model ◽  
Erlang Distributions ◽  
Periodic Review Inventory

This paper proposed a multi-item multi-source probabilistic periodic review inventory model under a varying holding cost constraint with zero lead time when: (1) the stock level decreases at a uniform rate over the cycle. (2) some costs are varying. (3) the demand is a random variable that follows some continuous distributions as (two-parameter exponential, Kumerswamy, Gamma, Beta, Rayleigh, Erlang distributions). The objective function under a constraint is imposed here in crisp and fuzzy environment. The objective is to find the optimal maximum inventory level for a given review time that minimize the expected annual total cost. Furthermore, a comparison between given distributions is made to find the optimal distribution that achieves the model under considerations. Finally, a numerical example is applied.

scholarly journals Multi-Source Backlogged Probabilistic Inventory Model for Crisp and Fuzzy Environment

2018 ◽  
Vol 14 (2) ◽  
pp. 7729-7743
Author(s):  
H. A. Ferganya ◽  
O. M. Hollahb
Keyword(s):  
Random Variable ◽  
Inventory Model ◽  
Inventory Level ◽  
Periodic Review ◽  
Fuzzy Environment ◽  
Holding Cost ◽  
Annual Total ◽  
Probabilistic Inventory Model ◽  
Erlang Distributions ◽  
Periodic Review Inventory

This paper proposed a multi-item multi-source probabilistic periodic review inventory model under a varying holding cost constraint with zero lead time when: (1) the stock level decreases at a uniform rate over the cycle. (2) some costs are varying. (3) the demand is a random variable that follows some continuous distributions as (two-parameter exponential, Kumerswamy, Gamma, Beta, Rayleigh, Erlang distributions).The objective function under a constraint is imposed here in crisp and fuzzy environment. The objective is to find the optimal maximum inventory level for a given review time that minimize the expected annual total cost. Furthermore, a comparison between given distributions is made to find the optimal distribution that achieves the model under considerations. Finally, a numerical example is applied.

scholarly journals A Probabilistic Inventory Model with Two-Parameter Exponential Deteriorating Rate and Pareto Demand Distribution

2018 ◽  
Vol 6 (5) ◽  
Author(s):  
Hala Aly Fergany ◽  
Osama Mahmoud Hollah
Keyword(s):  
Random Variable ◽  
Inventory Model ◽  
Inventory Systems ◽  
Systems Science ◽  
Inventory Level ◽  
Periodic Review ◽  
Deterministic Models ◽  
Demand Distribution ◽  
Deterioration Rate ◽  
Probabilistic Inventory Model

Although the deterioration is one of the main problems that have been investigated in the inventory systems science the last twenty years ago but Most deteriorating inventory studies focused on deterministic models. This paper presents a Constraint Deteriorating Probabilistic Periodic Review Inventory Model (CDPPRIM); a model is applicable when: (1) the demand is a random variable that follows Pareto distribution without lead-time, (2) some costs are varying, (3) shortages are permitted, and (4) the deterioration rate follows exponential distribution.The objective function under a constraint is imposed here in crisp and fuzzy environment. A numerical analysis method (Newton's method) is used to solve the model. The main objective is to find the optimal values of four decision variables (maximum inventory level, stock-out time, the deteriorating time and review time), which minimize the expected annual total cost under the assumptions. At the end, the paper explains the model through an application.

scholarly journals Optimization Of (s, S) Periodic Review Inventory Model With Uncertain Demand And Lead Time Using Simulation

2011 ◽  
Vol 15 (1) ◽  
Author(s):  
Raida Abuizam
Keyword(s):  
Lead Time ◽  
Inventory Model ◽  
Inventory Level ◽  
Periodic Review ◽  
Expected Cost ◽  
Ordering Policy ◽  
Long Time ◽  
Risk Simulation ◽  
Optimal Values ◽  
Periodic Review Inventory

This paper presents the use of Palisade @RISK simulation and RISKOptimizer to minimize the expected cost of inventory per period over a long time horizon. An (s, S) ordering policy will be used in this analysis. In an (s, S) ordering policy, an order is placed at the beginning of any period in which beginning inventory is less than s. The order size is the amount needed to bring the inventory level up to S. This paper illustrates a comparison between the use of @RISK simulation with trial values of (s, S) and the use of RISKOptimizer to find optimal values of (s, S) that minimize the expected cost of inventory over a period of time in a periodic review inventory model.

scholarly journals Variable demand model for periodically reviewing with allowing refunding parts of the orders

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
O. M. Hollah
Keyword(s):  
Field Study ◽  
Lead Time ◽  
Real Life ◽  
Random Variable ◽  
Inventory Level ◽  
Periodic Review ◽  
Demand Model ◽  
Time Intervals ◽  
Lagrange Multiplier Technique ◽  
Periodic Review Inventory

AbstractDepending on a field study for one of the largest iron and paints warehouses in Egypt, this paper presents a new multi-item periodic review inventory model considering the refunding quantity cost. Through this field study, we found that the inventory level is monitored periodically at equal time intervals. Returning a part of the goods that were previously ordered is permitted. Also, a shortage is permissible to occur despite having orders, and it is a combination of the backorder and lost sales. This model has been applied in both crisp and fuzzy environments since the fuzzy case is more suitable for real-life than crisp. The Lagrange multiplier technique is used for solving the restricted mathematical model. Here, the demand is a random variable that follows the normal distribution with zero lead-time. Finally, the model is followed by a real application to clarify the model and prove its efficiency.

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